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Statistical Evaluation of Surrogate Endpoints

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Statistical Evaluation of Surrogate Endpoints

Abstract: A surrogate endpoint is intended to replace a clinical endpoint for the evaluation of new treatments when it can be measured more cheaply, more conveniently, more frequently, or earlier than that clinical endpoint. A surrogate endpoint is expected to predict clinical benefit, harm, or lack of these. The ICH Guidelines on Statistical Principles for Clinical Trials state that “In practice, the strength of the evidence for surrogacy depends upon (i) the biological plausibility of the relationship, (ii) the demonstration in epidemiological studies of the prognostic value of the surrogate for the clinical outcome, and (iii) evidence from clinical trials that treatment effects on the surrogate correspond to effects on the clinical outcome”. We focus on the latter two conditions, and outline the statistical approaches that have been proposed to assess the extent to which these conditions are fulfilled. When data are available from a single trial, one can assess the “individual-level” association between the surrogate and the true endpoint. When data are available from multiple trials, one can additionally assess the “trial-level” association between the treatment effect on the surrogate and the treatment effect on the true endpoint. In the latter case, the “surrogate threshold effect” can be estimated as the minimum effect on the surrogate endpoint that predicts a statistically significant effect on the clinical endpoint. All these concepts are discussed in the context of randomized clinical trials in oncology, and illustrated with two recent meta-analyses in gastric cancer.